In recent decades, intensification of animal production has been occurring rapidly in transition economies to meet the growing demands of increasingly urban populations. This comes with significant environmental, health and social impacts. To assess these impacts, detailed maps of livestock distributions have been developed by downscaling census data at the pixel level (10 km or 1 km), providing estimates of the density of animals in each pixel. However, these data remain at fairly coarse scale and many epidemiological or environmental science applications would make better use of data where the distribution and size of farms are predicted rather than the number of animals per pixel. Based on detailed 2010 census data, we investigated the spatial point pattern distribution of extensive and intensive chicken farms in Thailand. We parameterized point pattern simulation models for extensive and intensive chicken farms and evaluated these models in different parts of Thailand for their capacity to reproduce the correct level of spatial clustering and the most likely locations of the farm clusters. We found that both the level of clustering and location of clusters could be simulated with reasonable accuracy by our farm distribution models. Furthermore, intensive chicken farms tended to be much more clustered than extensive farms, and their locations less easily predicted using simple spatial factors such as human populations. These point-pattern simulation models could be used to downscale coarse administrative level livestock census data into farm locations. This methodology could be of particular value in countries where farm location data are unavailable.